Title: Automatic number plate detection using deep learning

Authors: Madhura M. Bhosale; Yogesh S. Angal

Addresses: Department of Electronics and Telecommunication Engineering, JSPM's Rajarshi Shahu College of Engineering, Pune-411033, India ' Department of Electronics and Telecommunication Engineering, JSPM's Bhivarabai Sawant Institute of Technology & Research, Wagholi, Pune, India

Abstract: Automatic number plate recognition (ANPR) is a critical technology in intelligent transportation systems, playing a vital role in autonomous driving, traffic law enforcement, and vehicle monitoring. Accurate and efficient ANPR is essential for applications such as over-speed detection and automated traffic violation management. This paper presents a robust ANPR system leveraging YOLOv8, a state-of-the-art deep learning-based object detection model, for precise number plate localisation. To enhance character recognition accuracy, we employ advanced optical character recognition (OCR) techniques, specifically EasyOCR and PARSeq, ensuring reliable extraction of alphanumeric data from detected plates. Experimental results demonstrate that our system achieves approximately 96% accuracy in number plate detection and over 91% accuracy in character recognition, making it highly suitable for real-world deployment. The pro-posed approach enhances traffic surveillance, automated toll collection, and law enforcement by providing a high-speed, scalable, and accurate ANPR solution.

Keywords: automatic number plate detection; optical character recognition; OCR; EasyOCR; PARSeq; intelligent transportation systems; ITS; traffic surveillance; deep learning.

DOI: 10.1504/IJAISC.2025.151484

International Journal of Artificial Intelligence and Soft Computing, 2025 Vol.9 No.4, pp.343 - 359

Received: 25 Jun 2025
Accepted: 23 Oct 2025
Published online: 02 Feb 2026 *

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